A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/501 |
Resumo: | Highlights: Cellular Manufacturing systems cover a wide range of industries. Inflation rate can impose financial harms on cellular manufacturing systems. The over-allocation of workers, which usually happens in dynamic systems, causes reduction of the system performance. The proposed algorithm in this research can successfully schedule cellular systems to reduce system costs. Goal: The main aim is to determine the best trade-off values between in-house manufacturing and outsourcing, and track the impact of uncertain costs on gained schedules. To be more comprehensive, the performance of human resources is restricted and the partial demands are considered uncertain. Design / Methodology / Approach: In this paper a new method for minimizing human resource costs, including operating, salary, hiring, firing, and outsourcing in a dynamic cellular manufacturing system is presented where all system costs are uncertain during manufacturing periods and can be affected by inflation rate. For this purpose, a multi-period scheduling model that is flexible enough to use in real industries has been proposed. To solve the proposed model, a hybrid Ant Colony Optimization and the Tabu Search algorithm (ACTS) are proposed and the outcomes are compared with a Branch-and-Bound based algorithm. Results: Our findings showed that the inflation rate has significant effect on multi-period system planning. Moreover, utilizing system capability by the operator, for promoting and using temporary workers, can effectively reduce system costs. It is also found that workers’ performance has significant effect on total system costs. Limitations of the investigation: This research covers the cellular manufacturing systems. Practical implications: The algorithm is applied for 17 series of dataset that are found in the literature. The proposed algorithm can be easily applied in real industries. Originality / Value: The authors confirm that the current research and its results are original and have not been published before. The proposed algorithm is useful to schedule cellular manufacturing systems and analyse various production conditions. |
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Brazilian Journal of Operations & Production Management (Online) |
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A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costsHuman Resource SchedulingAnt Colony OptimizationSkilled Worker AssigningOut-SourcingUncertain CostsHighlights: Cellular Manufacturing systems cover a wide range of industries. Inflation rate can impose financial harms on cellular manufacturing systems. The over-allocation of workers, which usually happens in dynamic systems, causes reduction of the system performance. The proposed algorithm in this research can successfully schedule cellular systems to reduce system costs. Goal: The main aim is to determine the best trade-off values between in-house manufacturing and outsourcing, and track the impact of uncertain costs on gained schedules. To be more comprehensive, the performance of human resources is restricted and the partial demands are considered uncertain. Design / Methodology / Approach: In this paper a new method for minimizing human resource costs, including operating, salary, hiring, firing, and outsourcing in a dynamic cellular manufacturing system is presented where all system costs are uncertain during manufacturing periods and can be affected by inflation rate. For this purpose, a multi-period scheduling model that is flexible enough to use in real industries has been proposed. To solve the proposed model, a hybrid Ant Colony Optimization and the Tabu Search algorithm (ACTS) are proposed and the outcomes are compared with a Branch-and-Bound based algorithm. Results: Our findings showed that the inflation rate has significant effect on multi-period system planning. Moreover, utilizing system capability by the operator, for promoting and using temporary workers, can effectively reduce system costs. It is also found that workers’ performance has significant effect on total system costs. Limitations of the investigation: This research covers the cellular manufacturing systems. Practical implications: The algorithm is applied for 17 series of dataset that are found in the literature. The proposed algorithm can be easily applied in real industries. Originality / Value: The authors confirm that the current research and its results are original and have not been published before. The proposed algorithm is useful to schedule cellular manufacturing systems and analyse various production conditions.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2018-11-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articletext/htmlapplication/pdfhttps://bjopm.org.br/bjopm/article/view/50110.14488/BJOPM.2018.v15.n4.a4Brazilian Journal of Operations & Production Management; Vol. 15 No. 4 (2018): December, 2018; 499-5162237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/501/701https://bjopm.org.br/bjopm/article/view/501/703Copyright (c) 2018 Brazilian Journal of Operations & Production Managementinfo:eu-repo/semantics/openAccessDelgoshaei, AidinMirzazadeh, AbolfazlAli, Ahad2021-07-13T14:14:26Zoai:ojs.bjopm.org.br:article/501Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:18.398858Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
title |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
spellingShingle |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs Delgoshaei, Aidin Human Resource Scheduling Ant Colony Optimization Skilled Worker Assigning Out-Sourcing Uncertain Costs |
title_short |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
title_full |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
title_fullStr |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
title_full_unstemmed |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
title_sort |
A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs |
author |
Delgoshaei, Aidin |
author_facet |
Delgoshaei, Aidin Mirzazadeh, Abolfazl Ali, Ahad |
author_role |
author |
author2 |
Mirzazadeh, Abolfazl Ali, Ahad |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Delgoshaei, Aidin Mirzazadeh, Abolfazl Ali, Ahad |
dc.subject.por.fl_str_mv |
Human Resource Scheduling Ant Colony Optimization Skilled Worker Assigning Out-Sourcing Uncertain Costs |
topic |
Human Resource Scheduling Ant Colony Optimization Skilled Worker Assigning Out-Sourcing Uncertain Costs |
description |
Highlights: Cellular Manufacturing systems cover a wide range of industries. Inflation rate can impose financial harms on cellular manufacturing systems. The over-allocation of workers, which usually happens in dynamic systems, causes reduction of the system performance. The proposed algorithm in this research can successfully schedule cellular systems to reduce system costs. Goal: The main aim is to determine the best trade-off values between in-house manufacturing and outsourcing, and track the impact of uncertain costs on gained schedules. To be more comprehensive, the performance of human resources is restricted and the partial demands are considered uncertain. Design / Methodology / Approach: In this paper a new method for minimizing human resource costs, including operating, salary, hiring, firing, and outsourcing in a dynamic cellular manufacturing system is presented where all system costs are uncertain during manufacturing periods and can be affected by inflation rate. For this purpose, a multi-period scheduling model that is flexible enough to use in real industries has been proposed. To solve the proposed model, a hybrid Ant Colony Optimization and the Tabu Search algorithm (ACTS) are proposed and the outcomes are compared with a Branch-and-Bound based algorithm. Results: Our findings showed that the inflation rate has significant effect on multi-period system planning. Moreover, utilizing system capability by the operator, for promoting and using temporary workers, can effectively reduce system costs. It is also found that workers’ performance has significant effect on total system costs. Limitations of the investigation: This research covers the cellular manufacturing systems. Practical implications: The algorithm is applied for 17 series of dataset that are found in the literature. The proposed algorithm can be easily applied in real industries. Originality / Value: The authors confirm that the current research and its results are original and have not been published before. The proposed algorithm is useful to schedule cellular manufacturing systems and analyse various production conditions. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-24 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/501 10.14488/BJOPM.2018.v15.n4.a4 |
url |
https://bjopm.org.br/bjopm/article/view/501 |
identifier_str_mv |
10.14488/BJOPM.2018.v15.n4.a4 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/501/701 https://bjopm.org.br/bjopm/article/view/501/703 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Brazilian Journal of Operations & Production Management info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Brazilian Journal of Operations & Production Management |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 15 No. 4 (2018): December, 2018; 499-516 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051460948590592 |